Contrastive learning has been used for image classification to solve the issue of limited annotated images. However, commonly used contrastive learning methods have often focused on extracting image-level representations with a global contrastive loss [1]}. The use of image-level contrastive learning is fundamentally different from our patch-level approach. Image-level contrastive learning yields no information about intra-image variation or features, and thus is inapplicable to the common setting of analyzing different areas within an image. In contrast, image-level contrastive learning generally tries to solve the problem of whether two images both have the same property, like whether they are of the same person or not. Recent efforts have moved towards developing local pixel/patch-level representations that can be used for downstream tasks. In this study we focused on the latter: learning pixel level representations for segmentation tasks. However, an important difference is that our work uses unsupervised training end-to-end in obtaining its representations.
